Abstract
This paper investigates whether event-level pitch-bearing fatigue damage can be estimated directly from turbine measurements, and whether these mechanical damage metrics leave measurable fingerprints in the generator DC-link voltage and current. To achieve this, a case study was performed using SCADA and structural load data from the 45 kW Chalmers (Björkö) research turbine. This data was segmented into 223 park-run-park pitch events. For each event, blade-root flapwise and edgewise bending moments were converted into radial and axial loads at the pitch bearing; an equivalent dynamic bearing load Peq t was reconstructed using SKF and DG03 formulations; and rainflow counting with an S–N curve and Palmgren–Miner’s rule was used to compute event-level damage indices compatible with the International Standard Organization basic rating life concepts. In parallel, DC-link voltage and current were summarized into time-domain features, combined with operating-condition descriptors, and clustered using PCA-based k-means. The resulting clusters captured distinct electrical regimes that, across several event batches, corresponded to different levels of accumulated fatigue damage: regimes with sustained high DC-link voltage and longer duration tended to exhibit higher mean damage indices than lower, steadier DC regimes, indicating an electromechanical link. The results show that physics-based lifetime estimation and unsupervised analysis of existing electrical traces can be combined into a hybrid workflow for pitch-bearing condition assessment without additional sensors.